The most closely related works to our own are the following: (a) Our algorithmic approach in Section  is related to that of
[1]}, as both use discretization and linear programming to achieve an additive FPTAS. (b) [2]}, [3]} study constrained persuasion, but their constraints are on the complexity of the Sender-Receiver communication as measured by message length or number of signaled features and so are fundamentally different from ours.They also study a version called “bipartite signaling”, which has a combinatorial flavour different than ours, in an auction setting with the strong assumption that bidder values are known. [4]} considers persuasion by Sender who is constrained in the information she can acquire (and therefore, send) and characterizes the set of possible equilibrium outcomes. Our Theorem REF  is related to this literature in that it indicates that ex post constraints on persuasion do not cause a blowup in the number of signals needed to persuade optimally. (c) Inspired by [5]}, [6]} prove an upper bound on the required number of signals in an ex ante-constrained optimal scheme; we show that this bound is tight, give an alternative proof to the bound and provide an analogous tight bound for ex post constraints in Section . (d) [7]} studies a model of persuasion on compact subsets, which is equivalent to our ex post constraints; there is no parallel in that work to ex ante constraints, and the results on ex post in the two works do not overlap.
